Search Results - (( variable data optimization algorithm ) OR ( parameter optimization _ algorithm ))
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Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74% mean fitness function and 75.34% variable deviation indices enchantments compared to the traditional AOA. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms
Published 2017“…Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. …”
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Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA
Published 2014“…An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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5
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant
Published 2023Subjects:Conference paper -
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Parametric Optimization for the Maximization of Hydrogen Production by Enterobacter Cloacae
Published 2015“…By using data from the previous research, the parameters are optimized by using three numerical methods, simulated annealing, pattern search and Genetic algorithm. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology
Published 2015“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025Subjects:Article -
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Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. …”
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Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods
Published 2022“…Numerous research have reviewed and presented approaches for figuring out the PV models parameter optimization problem in the literature. However, few studies have been undertaken to construct the objective function, or no review papers have been published on the applied methodologies for solving the equations of nonlinear, multi-variable, and complicated PV models based on the datasheet information or actual experimental data. …”
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Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm
Published 2020“…Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. …”
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Multi-Objective Optimization of Minimum Quantity Lubrication in end Milling of Aluminum Alloy AA6061T6
Published 2015“…Analysis of variance at a 95% confidence level is implemented to identify the most significant input variables on the CNC end milling process. Optimization of the responses is done using a multi-objective genetic algorithm. …”
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Application of Hybrid Evolutionary Algorithm and thematic map for rule set generation and visualization of chlorophyta abundance at Putrajaya lake / Lau Chia Fong
Published 2013“…The rules generated by HEA are then integrated with thematic map technique for visualization of the Chlorophyta abundance. Input parameters are optimized using HEA to weed out insignificant input for predicting Chlorophyta abundance. …”
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